Archive for the ‘Unknown Risks’ category

Embedded assumptions are dangerous. That is because we are usually unaware and almost always not concerned about whether those embedded assumptions are still true or not.

One embedded assumption is that looking backwards, at the last year end, will get us to a conclusion about the financial strength of a financial firm.

We have always done that. Solvency assessments are always about the past year end.

But the last year end is over. We already know that the firm has survived that time period. What we really need to know is whether the firm will have the resources to withstand the next period. We assess the risks that the firm had at the last year end. Without regard to whether the firm actually is still exposed to those risks. When what we really need to know is whether the firm will survive the risks that it is going to be exposed to in the future.

We also apply standards for assessing solvency that are constant. However, the ability of a firm to take on additional risk quickly varies significantly in different markets. In 2006, financial firms were easily able to grow their risks at a high rate. Credit and capital were readily available and standards for the amount of actual cash or capital that a counterparty would expect a financial firm to have were particularly low.

Another embedded assumption is that we can look at risk based upon the holding period of a security or an insurance contract. What we fail to recognize is that even if every insurance contract lasts for only a short time, an insurer who regularly renews those contracts is exposed to risk over time in almost exactly the same way as someone who writes very long term contracts. The same holds for securities. A firm that typically holds positions for less than 30 days seems to have very limited exposure to losses that emerge over much longer periods. But if that firm tends to trade among similar positions and maintains a similar level of risk in a particular class of risk, then they are likely to be all in for any systematic losses from that class of risks. They are likely to find that exiting a position once those systematic losses start is costly, difficult and maybe impossible.

There are embedded assumptions all over the place. Banks have the embedded assumptions that they have zero risk from their liabilities. That works until some clever bank figures out how to make some risk there.

Insurers had the embedded assumption that variable products had no asset related risk. That embedded assumption led insurers to load up with highly risky guarantees for those products. Even after the 2001 dot com crash drove major losses and a couple of failures, companies still had the embedded assumption that there was no risk in the M&E fees. The hedged away their guarantee risk and kept all of their fee risk because they had an embedded assumption that there was no risk there. In fact, variable annuity writers faced massive DAC write-offs when the stock markets tanked. There was a blind spot that kept them from seeing this risk.

Many commentators have mentioned the embedded assumption that real estate always rose in value. In fact, the actual embedded assumption was that there would not be a nationwide drop in real estate values. This was backed up by over 20 years of experience. In fact, everyone started keeping detailed electronic records right after…… The last time when there was an across the board drop in home prices.

The blind spot caused it to take longer than it should have for many to notice that prices actually were falling nationally. Each piece of evidence was fit in and around the blind spots.

So a very important job for the risk manager is to be able to identify all of the embedded assumptions / blind spots that prevail in the firm and set up processes to continually assess whether there is a danger lurking right there – hiding in a blind spot.

Like this:

At least 75% of the US has experienced some Solar Risk this summer. Temperatures were into triple digits.

(in Fahrenheit. Fahrenheit is a part of the ancient measuring system that only America uses. 100F is 37.7C. Not so magical stated that way. But it is still exceptional.)

But very different solar risk is thought to be on the way. Solar Storms are thought to entering a busy season and to have the capability of wrecking havoc on various electromagnetic broadcast and receiving systems. GPS systems are thought to be particularly vulnerable.

The last major storm to hit earth reportedly caused the emerging telegraph systems in the US and Europe to encounter problems. We now depend upon many, many complex electronic systems.

But see what happens if you try to get your firm to prepare for violent solar storms. The best that may happen is that you would be laughed out of the room.

Like this:

What was the difference between the banks and insurers with high tech risk management programs that did extremely poorly in the GFC from those with equally high tech risk management programs who did less poorly?

One major difference was the degree to which they believed in their models. Some firms used their models to tell them exactly where the edge of the cliff was so that they could race at top speed right at the edge of the cliff. What they did not realie was that they did not know, nor could they know the degree to which the edge of that cliff was sturdy enough to take their weight. Their intense reliance on their models, most often models that focused like a lazer on the most important measure of risk, left other risks in the dark. And those other risks undermined the edge of the cliff.

Others with equally sophisticated models were not quite so willing to believe that it was perfectly safe right at the edge of the cliff. They were aware that there were things that they did not know. Things that they were not able to measure. Risks in the dark. They took the information from their models about the edge of the cliff and they decided to stay a few steps away from that edge.

They left something on the table. They did not seek to maximize their risk adjusted returns. Maximizing risk adjusted return in the ultimate sense involved identifying the opportunity with the highest risk adjusted return and taking advantage of that opportunity to the maximum extent possible, then looking to deploy remaining resources to the second highest risk adjusted return and so on.

The firms who had less losses in the crisis did not seek to maximize their risk adjusted return.

They did not maximize their participation in the opportunity with the highest risk adjusted return. They spread their investments around with a variety of opportunities. Some with the highest risk adjusted return choice and other amounts with lesser but usually acceptable return opportunities.

So when it came to pass that everyone found that their models were totally in error regarding the risk in that previously top opportunity, they were not so concentrated in that possibility.

They left something on the table and therefore had something left at the end of that round of the game.

Like this:

In the 1920’s, the French sought to protect themselves from future German invasions by building a wall across the most exposed route for such a tactic.

In the 1930’s, the Germans walked right around those fortifications and took France in short order.

Some financial firms have built Maginot Risk Management Systems. They consist of very fixed tests of risks and fixed processes for dealing with risks.

The US the Transportation Security Agency runs a Magniot Security system. Everyone knows what the security system is going to be when they get to the airport. So if anyone wants to get around it, it stays still or at best changes very slowly.

What is the alternative? Something that is flexible and variable. For security, what would happen if the airport security changed without notice, several times some days and not at all other weeks. You never know when it will change and what they will want next. Annoying to passengers, but probably infinitely more effective than the Maginot system now used.

And for risk management of a financial firm? What it needed there is flexibility and variability. The ability to look at things a number of different ways. The ability to answer new questions quickly. And the ability to ask new questions.

Like this:

“There is currently an upsurge in management’s willingness to listen to risk managers.” But Risk Managers consistently show a disturbing tendency towards projecting the next crisis from the last. Now in its fourth year, the Emerging Risks Survey from the Joint Risk Management Section and conducted by Max Rudolph.

Emerging risks are risks that are evolving in uncertain ways, have been forgotten in their dormancy, or are new. Emerging risks typically do not have a known distribution, that is their frequency is unknown.

In 2007, a shock to oil prices was seen as the top “emerging risk” in the first survey of risk managers. That year had seen a major spike in oil prices. In 2008, a blow-up in asset prices was identified as the top “emerging risk” immediately following the melt down of the sub prime market and a major drop in stock prices. In 2009, a fall in the value of the US dollar was identified as the top “emerging risk” at the end of a year when many major currencies had strengthened against the dollar. The new 2010 survey, released this week, indicates again that a fall in the US dollar is the top “emerging risk”.

If in fact these risk managers are advising their employers in the same way that they answer surveys, firms will continue to be well prepared for the last crisis and unprepared for the next one.

However, when asked to identify the single top emerging risk concern, a Chinese economic hard landing was the top pick with 14% of the respondents selecting that choice. That is certainly a scenario that has not just recently happened. So at least 14% of the respondents are doing some forward thinking.

Like this:

Frank Knight looked for the reason why firms are able to make a profit (in perfect competition situations that is) and he ultimately decided that firms were paid for UNCERTAINTY. He then went on to distinguish uncertainty from risk. Risk is the toss of the dice. With risk, the frequency & severity distribution of possible outcomes is known. Uncertainty differs fundamentally from risk because with uncertainty, the future likelihoods are unknown.

You are uncertain, to varying degrees, about everything in the future; much of the past is hidden from you; and there is a lot of the present about which you do not have full information. Uncertainty is everywhere and you cannot escape from it. Dennis Lindley

In risk management, we tend to treat everything as if it were a Knightian RISK and totally ignore UNCERTAINTY. We do our best job of estimating the frequency distribution of gains and losses and treat every best estimate the same. See Sins of Risk Measurement.

But we can and should make an effort to identify the uncertainty that lurks, to vastly differing degrees within our risk measures. A simple start to such an effort would be to develop a classification system for UNCERTAINTY.

Almost Totally Certain – like a prediction of time of sunrise. No experience contrary to predictions and good reason to believe that there will not be a regime change in the event. Highly unlikely that any human activity will fall into this category. Humans are just not this predictable.

Highly certain – like a prediction of the Cubs not winning the World Series. Never happened, but it is possible, but highly unlikely that there will be a regime change. Things in this category will be things that there is a long amount of historical evidence. The possibility of a fall in home prices were felt to fall into this category, but the historical evidence turned out to be from one single cycle. To put something in this category, a firm should have direct experience with the activity in question so that there is insight within the firm about the reasons for the historical drivers of the seemingly highly certain event.

Conditionally certain – Apple will stay successful as long as Jobs stays healthy (oops). For these sorts of uncertain events, the firm should have a that clear idea of the drivers of a string of predictable experience and an understanding that the driver(s) are not themself highly certain events.

Somewhat uncertain – “Bill says that it takes him 20 minutes to get to the airport” or “it usually takes me 20 minutes to get to the airport but sometimes it is an hour.” Here the firm either has only moderate amounts of experience to judge the actual uncertainty and the event seems to be fairly certain or else the firm has experience and knows that the event is somewhat uncertain.

Unknown uncertainty – “this is the first time I am parachute jumping and I plan to land in my backyard lawn chair.” Something new. With only limited knowledge of other people’s experiences and not enough experience to know whether there are significant differences in the drivers.

The first time a firm does an economic capital model, they might classify the result as having Level 5 uncertainty. Over time, some calculations might move up to Level 4 or Level 3. In a few areas, the firm might have been doing risk calculations for a particular risk over much longer time and could move up to Level 2 uncertainty there.

But change the question from an estimation of a 1-in-200 risk to a “will this project make money or not” question and is is quite possible that many of the answers might have Level 2 or Level 3 uncertainty.

But firms should try assigning Uncertainty ratings to their efforts. And track over time the degree to which the firm is devoting resources to projects with Level 5 Uncertainty.

Riskviews has worked for several firms that were over 100 years old at the time and those firms usually were very uncomfortable taking on any Level 5 Uncertainty. Most often they kept those activities small until they gained experience. When they went for long periods of time with no Level 5 Uncertainty, however, they tended to shrink relative to the rest of the industry.

On the other hand, the financial crisis was touched off by Banks and other institutions who committed to enough Level 4 and Level 5 uncertainty to send them over the edge. Investors would certainly be interested to know how much Level 5 Uncertainty that a firm is taking at any point in time.

Using an Uncertainty scale like this and discussing the reasons for changes to the level of commitment to higher uncertainty projects will be a healthy and productive exercise for many firms.

Can management tell them exactly what sorts of events could put the firm out of business? Have they discussed the sorts of “highly unlikely” events that might take the firm down if they suddenly did happen?

Those are, of course, the conversations that the board might well demand to have if they really understood that Survival is not Mandatory.